The present invention relates to the flat panel displays based on liquid crystal (LC) technology, and more particularly to the inspection of components formed on such displays.
During the manufacturing of LC displays, large clear plates of thin glass are used as a substrate for the deposition of thin film transistor (TFT) arrays. Usually, several independent TFT arrays are contained within one glass substrate plate and are often referred to as TFT panels.
TFT pattern deposition is performed in a multitude of stages where in each stage, a particular material (such as a metal, indium tin oxide (ITO), crystalline silicon, amorphous silicon, etc.) is deposited on top of a previous layer (or glass) in conformity with a predetermined pattern. Each stage typically includes a number of steps such as deposition, masking, etching, stripping, etc.
During each of these stages and at various steps within each stage, many production defects may occur that may affect the electrical and/or optical performance of the final LCD product. Such defects include but are not limited to metal protrusion 110 into ITO 112, ITO protrusion 114 into metal 116, a so-called mouse bite 118, an open circuit 120, a short 122 in a transistor 124, and a foreign particle 126, as shown in FIG. 1. Other defects include mask problems, and over or under etching.
Even though the TFT deposition processes are tightly controlled, defect occurrence is unavoidable. This limits the product yield and adversely effects production costs. Typically, the TFT arrays are inspected using one or multiple Automated Optical Inspection (AOI) system(s) following critical deposition stages and by an opto-electrical inspection machine, also referred to as array checker (AC), to test the finished TFT arrays. Commonly AOI and AC systems provide defect coordinates; they do not provide high resolution images required to classify defects as killer, repairable or just imperfections not affecting the TFT array performance (so called process defects). The defect coordinate information is passed to a TFT array repair tool, also referred to as array saver (AS), and such classification is conventionally done manually by the TFT array repair machine operator.
The average number of defects per plate may vary from one TFT array manufacturer to another and from one manufacturing plant to another. Typically, the defect review and repair capacity within the TFT array fabrication line is sized to process 300-400 defects per 7th generation plates. Typically 5 to 10% of defects per plate are assumed to require repair.
Since the TFT array features are typically very small (typical sub-pixel size is 80×240 μm), the defect review—to decide whether the defect is repairable—is performed using a microscope. The microscope field of view is small (ranging from 100×100 μm to 2×2 mm) relative to the plate size (typically 2.1×2.4 m). As shown in FIG. 2, the microscope is installed on a precision XY stage so that it could be dispatched from one defect to another. The defect coordinates are known from inspections carried out earlier by AOI and AC inspection systems. The glass plate remains immobilized under the XY stage by means of a vacuum chuck during the defect review and repair. Following the review, the repairable defects are typically treated by means of laser trimming, laser welding or by bridging open line defects typically using a chemical vapor deposition (CVD) technique.
Depth of focus range of the microscope can be as small as, for example, +/−0.6 microns when a high magnification is used. Maintaining such Z position of the plate relative to the inspection or repair optics is difficult because of, for example, (a) the relatively large plate size, (b) the variations in plate thickness, (c) non-zero Z variations in the inspection/repair stage as it moves in X and Y, and (d) non-zero Z variations in plate holder (chuck) over the expanse of the plate. As a result, after dispatch to a new defect location, the microscope needs to be refocused to provide a sharp image for defect review and to enable acquisition of the required laser spot size to facilitate proper laser trimming. Consequently, focusing of the microscope is always performed at the new defect location and is done automatically. Typically the auto-focusing action lasts approximately on the order of seconds. During this period, the instrument is neither used for defect review nor for laser trimming and thus the instrument remains idle. With typically 400 defects per plate, the auto-focusing consumes approximately 400 seconds of instrument idle time. The idle time undermines the efficiency of instrument utilization. Reducing or eliminating the auto-focusing periods becomes particularly important when the array saver instrument is equipped with an automatic defect repair capability, i.e., repair without operator assistance.
FIG. 2 shows an exemplary TFT array repair machine. In FIG. 2, reference numerals 202, 204, 206, 208, and 210 respectively identify a granite base, a gantry providing Y motion, an X motion carriage, a microscope and laser tool capable of moving in Z direction for focusing, and a chuck adapted to support and immobilize glass plates.
In some known TFT array repair, or array saver instruments, the microscope is equipped with an area scan charge coupled device (CCD) camera for recording the review images and displaying them to the operator on a monitor. Digital image processing (DIP) of the recorded images is subsequently used to extract information about the degree to which auto-correction of the focus may be required. Several DIP algorithms are in wide use for deriving the focus quality criterion (FQC). Most of these algorithms are based on the observation that a sharp, in-focus image exhibits the highest content of high spatial frequency components. Typically DIP algorithms include the following steps: i) normalization of the image intensity; ii) application of high pass digital filter to the image (e.g., the Laplacian operator); iii) application of an absolute value operator to the filtered image; iv) integration (summing) of all the pixel intensity values to obtain the FQC value for the processed image.
FIG. 3 illustrates the FQC as a function of the vertical (Z) position of a microscope objective lens and derived from an array image over the range of ±20 μm around the best focus point. A ×20 objective lens magnification and an aperture of 0.42 were used to generate FIG. 3. Each dot on the curve corresponds to a separate image. In the example of FIG. 3, the best focus, judged visually, coincides with the maximum value of FQC at −2.5 μm.
The DIP based auto-focusing method is relatively simple and inexpensive and requires no additional hardware; however, it suffers from a number of number of deficiencies, a few of which are described below. First, high contrast features are needed in the imaged scene for computing the FQC. Therefore, the DIP method fails on blank or almost blank images. Second, a single sample of the FQC does not indicate whether the microscope is above or below the best focus point. Also, a single sample of FQC is insufficient to determine the distance that will achieve the best focus position. Therefore, the DIP method requires more than one image to deduce best focus. Third, outside of a relatively narrow range (e.g., ±20 μm for ×20 objective) the FQC becomes non-monotonic. Thus, even multiple samples of FQC often do not indicate the direction towards the best focus position.
To overcome some of the above described deficiencies, a microscope is moved far enough from the best focus position, to ensure that it resides on the known side of the best focus position. The FQC curve is captured concurrently as the microscope is moved towards the best focus position. During this process, the microscope passes beyond the best focus point to enable locating the maximum point of the related FQC curve. The FQC maximum point is typically computed using interpolation between the captured FQC sample points to improve focusing precision. Subsequently, the microscope is reversed to the position corresponding to the computed FQC maximum point, thereby to provide the near best focus position.
As is well known, the DIP method, notwithstanding the above developments, is slow, and depending on the required auto-focus range and the CCD camera frame rate, may take 1 to 5 seconds to complete the auto-focusing task. For instance if the required auto-focus range is ±150 μm, the FQC is sampled every 5 μm and the camera frame rate is 30 frames per second, capturing FQC over this range can not be done faster then 2 seconds.
One method to optimizing the search for the FQC is to reduce the number of the FQC samples required to find the FQC maximum so as to reduce the auto-focus time. Such a search may be carried out by performing a relatively coarse and fast search over the entire auto-focus range and subsequently performing a finer search in the vicinity of the best focus point. However, even with the optimized search, an auto-focus time below one second may not be achieved.
In accordance with another well known technique, instead of digitally processing the images, the analog composite video signal is processed by an analog high-pass filter and then digitized for computing the FQC. This technique reduces the amount of computation required to obtain the FQC curve, but because the FQC curve needs to be sampled image by image, it suffers from the same drawbacks as the DIP technique. The auto-focusing time achievable using these techniques is approximately 1 second.
FIG. 4 is a schematic representation of another DIP based auto-focus sensor, referred to as a Line Scan DIP sensor (LS-DIP). In FIG. 4, reference numeral 402 represents a defect review camera (area scan CCD), reference numeral 404 represents an image of the structured light illuminated object plane, reference numeral 406 represents a tube lens, reference numeral 408 represents a line scan CCD sensor beam splitter, reference numeral 410 represents a structured light illuminator beam splitter, reference numeral 412 represents a microscope objective lens, reference numeral 414 represents a structured light illuminated object plane, reference numeral 416 represents an object plane, reference numeral 418 represents a line scan camera tube lens, reference numeral 420 represents an image of the structured light illuminated object plane on the line scan image sensor, reference numeral 422 represents a line scan image sensor, reference numeral 424 represents a structured light projection tube lens, reference numeral 426 represents a slit array, reference numeral 428 represents a high intensity light source (for instance super luminescent light emitting diode), reference numeral 430 represents a magnified view of structured light projected onto the object plane, and reference numeral 432 represents a section of the object plane imaged onto the line scan image sensor.
The LS-DIP technique is based on a principle similar to the previously described DIP methods. The technique requires a FQC curve be captured by shifting the microscope along the Z axis and then computing the in-focus Z coordinate corresponding to the FQC curve maximum. Auto-focusing time is somewhat reduced by using the line scan sensor instead of the area scan array for capturing the FQC. For instance, a 512 pixels line scan sensor with 40 MHz pixel clock, can be read every 15 micro-seconds; this corresponds to 66,666 frames per second. Using this frame rate, the time required for capturing the FQC curve is made dependent on the speed of the Z (focusing) motion actuator rather than on the camera frame rate. Since there is no guarantee that the section of the object plane imaged onto the line scan image sensor includes a sufficient number of high contrast features for computing a meaningful FQC, a structure light pattern is projected onto the object plane. Projecting structured light onto the object plane also makes the LS-DIP method usable on plane, featureless objects. Typically with the LS-DIP method, the auto-focus requires approximately 0.5 seconds.
Conventional DIP based auto-focus methods share the common disadvantage of requiring scan along the Z axis to capture the FQC curve. During the scan period, the microscope focus state is undetermined and the auto-focus sequence is launched only upon the arrival at the new defect review location. Thus, the auto-focus time always delays the review process. Moreover because the DIP methods are not capable of maintaining focus during the microscope motion within the XY plane, they are not suitable for on-the-fly rapid defect image capture with a strobe light illumination that freezes the microscope motion to prevent image smearing.